Economist article on new low-end warfare models

This article in the Economist discusses 6 models. I cut and paste from the article the following summaries:

SCARE, the Spatio-Cultural Abductive Reasoning Engine, developed at the United States Military Academy at West Point by a team led by Major Paulo Shakarian. Major Shakarian and his team have analysed the behaviour of guerrillas in both Iraq and Afghanistan, and think they understand it well enough to build reliable models. Using the co-ordinates of previously bombed sites, data from topographical and street maps, and information on an area’s ethnic, linguistic and confessional “human terrain”, SCARE is able to predict where guerrillas’ munition dumps will be to within about 700 metres.

RiftLand is being developed on the navy’s behalf by Claudio Cioffi-Revilla, a professor of computational social science at George Mason University in Virginia. It is specific to the part of East Africa around the Great Rift Valley (hence the name). That this area includes Congo, Ethiopia, Rwanda, Somalia and Uganda, each of which has been the scene of present or recent civil strife, is no coincidence. Broadly, RiftLand works by chewing its way through a range of data collected by charities, academics and government agencies, and uses these to predict where groups of people will go and with whom they may clash in times of drought or armed conflict.

Condor has been developed by Peter Gloor of the Massachusetts Institute of Technology. It works by sifting through data from Twitter, Facebook and other social media, and using them to predict how a public protest will evolve. It does so by performing what Dr Gloor calls “sentiment analysis” on the data. Condor, then, is good at forecasting the course of existing protests. Even better, from the politicians’ point of view, would be to predict such protests before they occur.

E-MEME (Epidemiological Modelling of the Evolution of MEssages) uses sentiment analysis to see how opinions and states of mind flow across entire populations, not just activists. It is a product of Aptima of Woburn MA. It employs data from online news sources, blogs and Twitter, and attempts to rank the “susceptibility” of certain parts of the populace to specific ideas.

The Worldwide Integrated Crisis Early Warning System (W-ICEWS) project, led by Lockheed Martin, a large American defence contractor, goes even further. According to Lieutenant-Colonel Melinda Morgan … it can crunch great quantities of data from digital news media, blogs and other websites, and also intelligence and diplomatic reports. It then uses all this to forecast—months in advance—riots, rebellions, coups, economic crises, government crackdowns and international wars. Colonel Morgan calls this process “social radar”.

Venkatramana Subrahmanian of the University of Maryland proposes something more specific. The Temporal-Probabilistic Rule System, a program his team has developed using $600,000 of American-army money, looks at 770 social and political indicators and uses them to predict attacks by Lashkar-e-Taiba, a guerrilla group based in Pakistan-administered Kashmir. If it works, this process might be applied, using a different set of indicators, to other groups of rebels. The crucial point about Dr Subrahmanian’s model is that it not only predicts attacks, it also suggests how they might be countered.

I’m highly skeptical of many of the claims, particularly those, like the W-ICEWS that seem to aim at producing “the Machine” from the “Person of Interest” TV show…

Anyway I await being knocked from my skepticism by some of these things working, though if they do i think it will mean we will have to change some of what we think we know about complex-adaptive systems, or just how complex and adaptive human systems actually are… Either is possible!


About Paul Vebber

"If you read about something, you have learned about it. If you can teach something, you have mastered it. Designing a useful game about something however, requires developing a deep understanding of how it relates to other things."
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